def main(output_file, file_recording_raw, file_recording_processed, file_sorting, file_metadata):
# Source data
source_data = dict(
BlackrockRaw=dict(filename=file_recording_raw),
BlackrockProcessed=dict(filename=file_recording_processed),
BlackrockSorting=dict(
filename=file_sorting,
nsx_to_load=6
)
)
# Initialize converter
converter = MovshonBlackrockNWBConverter(source_data=source_data)
# Get metadata from source data and modify any values you want
metadata = converter.get_metadata()
if file_metadata:
m = load_metadata_from_file(file_metadata)
metadata = dict_deep_update(metadata, m)
converter.validate_metadata(metadata)
# Get conversion options and modify any values you want
conversion_options = converter.get_conversion_options()
# Run conversion
converter.run_conversion(
metadata=metadata,
nwbfile_path=output_file,
save_to_file=True,
overwrite=True,
conversion_options=conversion_options
)
def main(output_file, file_raw, file_lfp, file_expo, file_ttl, file_metadata):
# Source data
source_data = dict(SpikeGLXRaw=dict(file_path=str(file_raw)),
SpikeGLXLFP=dict(file_path=str(file_lfp)),
ExpoDataInterface=dict(expo_file=str(file_expo),
ttl_file=str(file_ttl)))
# Initialize converter
converter = MovshonSpikeglxNWBConverter(source_data=source_data)
# Get metadata from source data and modify any values you want
metadata = converter.get_metadata()
if file_metadata:
m = load_metadata_from_file(file_metadata)
metadata = dict_deep_update(metadata, m)
converter.validate_metadata(metadata)
# Get conversion options and modify any values you want
conversion_options = converter.get_conversion_options()
# Run conversion
converter.run_conversion(metadata=metadata,
nwbfile_path=output_file,
save_to_file=True,
overwrite=True,
conversion_options=conversion_options)
def main(output_file, dir_oephys, file_expo, file_ttl, file_metadata):
# Source data
source_data = dict(
OpenEphysRecordingExtractorInterface=dict(folder_path=str(dir_oephys)),
ExpoDataInterface=dict(expo_file=str(file_expo),
ttl_file=str(file_ttl)))
# Initialize converter
converter = MovshonOpenEphysNWBConverter(source_data=source_data)
# Get metadata from source data and modify any values you want
metadata = converter.get_metadata()
if file_metadata:
m = load_metadata_from_file(file_metadata)
metadata = dict_deep_update(metadata, m)
converter.validate_metadata(metadata)
# Get conversion options and modify any values you want
conversion_options = converter.get_conversion_options()
# Run conversion
converter.run_conversion(metadata=metadata,
nwbfile_path=output_file,
save_to_file=True,
overwrite=True,
conversion_options=conversion_options)
def convert_session(session_path: FolderPathType, nwbfile_path: FilePathType):
"""Wrap converter for Parallel use."""
print(f"Processsing {session_path}...")
session_name = session_path.stem
subject_name = session_path.parent.name
dat_file_path = session_path / f"{session_name}.dat"
eeg_file_path = session_path / f"{session_name}.eeg"
source_data = dict(
NeuroscopeRecording=dict(file_path=str(dat_file_path),
gain=conversion_factor),
NeuroscopeLFP=dict(file_path=str(eeg_file_path),
gain=conversion_factor),
YutaVCBehavior=dict(folder_path=str(session_path)),
PhySorting=dict(folder_path=str(session_path),
exclude_cluster_groups=["noise", "mua"]),
)
converter = YutaVCNWBConverter(source_data=source_data)
conversion_options = dict(
NeuroscopeRecording=dict(stub_test=stub_test,
es_key="ElectricalSeries_raw"),
NeuroscopeLFP=dict(stub_test=stub_test),
PhySorting=dict(stub_test=stub_test),
)
metadata = converter.get_metadata()
metadata["Subject"].update(genotype=subject_genotypes[subject_name])
metadata_from_yaml = load_metadata_from_file(metadata_path)
metadata = dict_deep_update(metadata, metadata_from_yaml)
converter.run_conversion(nwbfile_path=str(nwbfile_path),
conversion_options=conversion_options,
metadata=metadata,
overwrite=True)
sys.stdout.flush() # Needed for verbosity in Parallel
def test_load_metadata_from_file():
m0 = dict(NWBFile=dict(
experimenter='Mr Tester',
identifier='abc123',
institution='My University',
lab='My lab',
session_description='testing conversion tools software',
session_start_time='2020-04-15T10:00:00+00:00'),
Subject=dict(description='ADDME',
sex='M',
species='ADDME',
subject_id='sid000',
weight='10g',
date_of_birth='2020-04-07T00:15:00+00:00'),
Ecephys=dict(Device=[dict(name='device_ecephys')],
ElectricalSeries=[
dict(description='ADDME',
name='ElectricalSeries',
rate=10.0,
starting_time=0.0,
conversion=1.0)
],
ElectrodeGroup=[
dict(description='ADDME',
device='device_ecephys',
location='ADDME',
name='ElectrodeGroup')
]))
yaml_file = os.path.join(os.path.dirname(__file__), 'metadata_tests.yml')
json_file = os.path.join(os.path.dirname(__file__), 'metadata_tests.json')
m1 = load_metadata_from_file(file=yaml_file)
compare_dicts_2(m0, m1)
m2 = load_metadata_from_file(file=json_file)
compare_dicts_2(m0, m2)